Enabling Science through Data (Big and Otherwise)

NIH’s recent focus on data-intensive and data-driven biomedical research makes this an exciting time for me to be joining NIGMS and leading its Division of Biomedical Technology, Bioinformatics, and Computational Biology (BBCB).

New steps toward harnessing the power of data began well before my arrival and include the NIH Big Data to Knowledge (BD2K) initiative. The overarching aim of this initiative is to enable, by the end of this decade, a “quantum leap” in the ability of the biomedical and behavioral research enterprise to use the growing volume of complex data to produce important insights into biological systems. This is an ambitious goal that requires the collective engagement and expertise of NIH’s many institutes, centers, and offices, including NIGMS, as well as the scientific community.

My colleagues from across NIH have already come together to discuss future solutions that will benefit NIH and the research community as a whole. We recognize that no one-size-fits-all solution will emerge as the “data quantum leap.” Our hope is that by engaging academic, industrial and other biomedical stakeholders, we will impact the volume, variety, velocity, viability and ultimately value of the data that NIH invests in.

To jumpstart this activity, NIH recently issued a new funding opportunity announcement (FOA) for Centers of Excellence for Big Data Computing in the Biomedical Sciences. The purpose is to establish an interactive consortium of centers that will develop approaches, methods and software tools for the aggregation, integration, analysis and visualization of data across NIH-funded research areas. NIH also has issued a request for information on the development of analysis methods and software for big data; responses are due by September 6.

NIGMS and the BBCB staff were actively involved in crafting the new FOA and, more generally, have played a central role in the creation and organization of the BD2K initiative. We will continue to be active partners in this endeavor.

Big data is just one example of the division’s efforts. We foster research in a range of fields, including computational biology, bioinformatics, mathematical and statistical biology, and biomedical technology development. We also support programs that train people in many of these areas.

I’m so happy to be involved in shaping the division’s activities, and I look forward to working together with many of you to continue innovating basic biomedical research.

One comment on “Enabling Science through Data (Big and Otherwise)

  1. Here are comments by my son who works in Silicon Valley on Big Data projects-
    “First, they seem to be saying that the main impediment to better data science // biomedical research is a lack of information and organization. They seem to be trying to create an easy-to-access library of data from these sorts of experiments where people can build on each others’ data (rather than just their conclusions).

    That does seem to be the right approach, as big data processing — following CS in general — is based on a bunch of people working on different parts of the puzzle (databases, distributed computing, analytic algorithms, visualization, insights) and ensuring interoperability. So it’s good to see they recognize this.

    Second, maybe I’ve just gotten used to Silicon Valley-style information presentation, but it doesn’t seem like there’s a very good sense of what needs to be done.

    Other than the typical difficult-to-read page layout, there wasn’t a very good sense of what a success story would look like, such as “here are some fields who have done similar things in the past.”

    No mention was made of tools that might be used, and when I see sentences like “Research on how to release, process, aggregate, integrate, visualize, and analyze Big Data, including data from disparate research areas” — I have to wonder if they know all the progress that has been made on that front — I can name ten or fifteen companies and open-source products in this area.

    Third, the whole approach seems a bit on the bureaucratic/managerial side. They’ve been recruiting an Associate Director for eight months, unsuccessfully. Reading the current acting director’s bio, it seems to be written as if he’s trying to become a university president rather than an innovative researcher, where typical data scientists take the opposite tack. Add in the way information is presented, timeframes (submit proposals now, start work by July of next year), etc.

    I’m sure this is the typical speed of things at the NIH; on the other hand, my current boss is in charge of Architecture for XXX, and he said he tends to overhaul the system every eighteen months.

    I wish them well, but am skeptical about the chances of progress. …”

    Hope that this helps.

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